Even role prompting is totally useless imo. Maybe it was a thing with GPT3, but most of the LLMs already know they're "expert programmers". I think a lot of people are just deluding themselves with "prompt engineering".
Be clear with your requirements. Add examples, if necessary. Check the outputs (or reasoning trace if using a reasoning model). If they aren't what you want, adjust and iterate. If you still haven't got what you want after a few attempts, abandon AI and use the reasoning model in your head.
It's become more subtle but still there. You can bias the model towards more "expert" responses with the right terminology. For example, a doctor asking a question will get a vastly different response than a normal person. A query with emojis will get more emojis back. Etc.
This is definitely something I’ve noticed — it’s not about naïve role-priming at all, but rather about language usage.
“You are an expert doctor, help me with this rash I have all over” will result in a fairly useless answer, but using medical shorthand — “pt presents w bilateral erythema, need diff dx” — gets you exactly what you’re looking for.
I get the best results with Claude by treating the prompt like a pseudo-SQL language, treating words like "consider" or "think deeply" like keywords in a programming language. Also making use of their XML tags[1] to structure my requests.
I wouldn't be surprised if in a few years from now some sort of actual formalized programming language for "gencoding" AI is gonna emerge.
One thing I've had a lot of success with recently is a slight variation on role-prompting: telling the LLM that someone else wrote something, and I need their help assessing the quality of it.
When the LLM thinks _you_ wrote something, it's nice about it, and deferential. When it thinks someone else wrote it, you're trying to decide how much to pay that person, and you need to know what edits to ask for, it becomes much more cut-throat and direct.
I notice this to affect its tendency to just make things up in other contexts, too. I asked it to take a look at "my" github, gave it a link, then asked it some questions; it started talking about completely different repos and projects I never heard of. When I simply said take a look at `this` github and gave it a link, its answers had a lot more fidelity to what was actually there (within limits of course - it's still far from perfect) [This was with Gemini Flash 2.5 on the web]. I have had simlar experiences asking it to do style transfer from an example of "my" style versus "this" style, etc. Presumably this has something to do with the idea that in training, every text that speaks in first person is in some sense seen as being from the same person.
Be clear with your requirements. Add examples, if necessary. Check the outputs (or reasoning trace if using a reasoning model). If they aren't what you want, adjust and iterate. If you still haven't got what you want after a few attempts, abandon AI and use the reasoning model in your head.